Blind channel identification (BCI) refers to the estimation of channel impulse response from only its output. Over the past few years, this area of research has received immense practical interest (see, e.g., Tong, et al., “Multichannel Blind Identification: From Subspace to Maximum Likelihood Methods,” Proc. IEEE, vol. 86, no. 10, pp. 1951-1968, October 1998, and references therein). The primary reason for this is the fact that BCI does not require a training sequence to equalize the channel, thereby saving the channel capacity, which is severely limited in mobile communications. Some early research in this direction focused on using higher-order statistics (HOS), which require the use of long data records for reliable estimates. This limits their usage in mobile communications where the channel is desired to be estimated within a short period of time.
Blind estimation of FIR channels using only second-order statistics (SOS) is first attributed to Tong, et al., who showed that by sampling the received data at a rate higher than the baud (symbol) rate, SOS can suffice to estimate the channel impulse response up to a constant (Tong, et al., “A New Approach to Blind Identification and Equalization of Multipath Channels,” in Proc. 25th Asilomar Conf. Signals, Syst. Comp., 1991, pp. 856-860). Since then, many different statistical and deterministic approaches for BCI have been studied, each having its own relative merits and demerits (e.g., Tong, et al., “Multichannel . . . ,” supra).